Related Experiment Video
Updated: Dec 5, 2025
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated
Junaed Younus Khan1, Md Tawkat Islam Khondaker1, Iram Tazim Hoque1
1Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Background:
Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion.
Objective:
The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach.
Methods:
We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes.
Results:
Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base.
Conclusions:
Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.
Insights
This study used computational methods to analyze scientific literature on COVID-19 treatments. It identified potential drugs like Remdesivir and Dexamethasone, and created an open-source knowledgebase called COVID-19Base.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Pharmacology
Background:
- The COVID-19 pandemic necessitates rapid identification of effective treatments.
- Existing research on drug repurposing and therapeutic strategies is vast but fragmented.
- A systematic approach is needed to consolidate evidence on potential COVID-19 treatments.
Purpose of the Study:
- To computationally explore potential drugs and biomedical entities for coronavirus diseases, including COVID-19, from scientific literature.
- To develop a knowledgebase for systematic exploration of therapeutic opportunities.
- To identify and evaluate existing drugs for their efficacy in treating COVID-19.
Main Methods:
- Literature mining of publicly available scientific data and resources.
- Integration of six topic-specific dictionaries (genes, miRNAs, diseases, Protein Data Bank, drugs, drug side effects).
- Application of natural language processing, sentiment analysis, deep learning, and cosine similarity for evidence extraction and association inference.
Main Results:
- Identification of 1805 diseases, 2454 drugs, and 1910 genes related to coronavirus diseases.
- Development of COVID-19Base, an open-source knowledgebase for COVID-19 research.
- Identification of Remdesivir, Statins, Dexamethasone, and Ivermectin as potential effective COVID-19 treatments, while Hydroxychloroquine was deemed ineffective.
Conclusions:
- The developed knowledgebase and identified interactions facilitate the discovery of novel therapeutic strategies for COVID-19.
- Further investigation of mined biomedical entities can accelerate the development of effective COVID-19 treatments.
- The computational approach provides a valuable tool for researchers in the fight against COVID-19.
More Related Videos
05:47Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
05:50Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
Published on: September 26, 2025
Related Concept Videos
01:26Drug Discovery: Overview
01:28Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...